11.3.4.3 Local binary pattern features The local binary pattern (LBP) was first proposed in 1994. It is a rudimentary yet extremely efficient technique used in texture-based analysis of an image. The various st
Geometric local binary patternLocal derivative patternAn obvious way of digital image forgery is a copy-move attack. It is quite simple to carry out to hide important information in an image. Copy-move process contains three main steps: copy the fragment from one place of an image, transform ...
The proposed method JSR-LBP mainly includes the following steps: First, the JSRC is used to obtain the representation residuals of different pixels. Then, the LBP value is calculated for every pixel in the whole image to generate the LBP histogram; the LBP histogram of the test sample can ...
2. The region detection and normalization steps are described in Section 5.1. In our experiments, the region size after normalization is fixed to 41×41 pixels and the pixel values lie between 0 and 1. Experimental evaluation We use two well-known protocols to evaluate the proposed CS-LBP ...
For each neighbor, if the neighbor’s entropy is greater than or equal to the center pixel’s entropy, assign a binary value of 1; otherwise, it is 0. This forms a binary pattern for each pixel. The binary pattern can be converted into a decimal number. This number represents the LB...
General steps of local SMQT-based face detection[16]. Full size image Figure4shows the images of the three faces from Oulu face database enhanced by using DWT + SVE illumination enhancement and the segmented faces by using local SMQT. ...
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Local binary pattern works on local features that uses LBP operator which summarizes the local special The first two steps are shared by all the algorithm [8]. Face recognition is not a simple problem since an unknown face image seen in the extraction phase is usually different from the face...
Traditional machine vision-based detection methods usually involve two main steps: feature extraction and classification. These methods first extract features such as texture1 and spectrum2,3,4 and then employ classifiers like SVM5, ELM6, or clustering7,8,9 to perform detection tasks. However, ...
First, a block multi-scale uniform local binary pattern (MULBP) features operator based on improved circular neighborhood is employed to extract the local texture features of finger vein images effectively. Then two-directional two-dimension principal component analysis ((2D)2PCA) method is applied ...